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        "%matplotlib inline"
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        "\n# A demo of structured Ward hierarchical clustering on an image of coins\n\n\nCompute the segmentation of a 2D image with Ward hierarchical\nclustering. The clustering is spatially constrained in order\nfor each segmented region to be in one piece.\n\n"
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      "source": [
        "# Author : Vincent Michel, 2010\n#          Alexandre Gramfort, 2011\n# License: BSD 3 clause\n\nprint(__doc__)\n\nimport time as time\n\nimport numpy as np\nfrom distutils.version import LooseVersion\nfrom scipy.ndimage.filters import gaussian_filter\n\nimport matplotlib.pyplot as plt\n\nimport skimage\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.feature_extraction.image import grid_to_graph\nfrom sklearn.cluster import AgglomerativeClustering\n\n# these were introduced in skimage-0.14\nif LooseVersion(skimage.__version__) >= '0.14':\n    rescale_params = {'anti_aliasing': False, 'multichannel': False}\nelse:\n    rescale_params = {}\n\n# #############################################################################\n# Generate data\norig_coins = coins()\n\n# Resize it to 20% of the original size to speed up the processing\n# Applying a Gaussian filter for smoothing prior to down-scaling\n# reduces aliasing artifacts.\nsmoothened_coins = gaussian_filter(orig_coins, sigma=2)\nrescaled_coins = rescale(smoothened_coins, 0.2, mode=\"reflect\",\n                         **rescale_params)\n\nX = np.reshape(rescaled_coins, (-1, 1))\n\n# #############################################################################\n# Define the structure A of the data. Pixels connected to their neighbors.\nconnectivity = grid_to_graph(*rescaled_coins.shape)\n\n# #############################################################################\n# Compute clustering\nprint(\"Compute structured hierarchical clustering...\")\nst = time.time()\nn_clusters = 27  # number of regions\nward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward',\n                               connectivity=connectivity)\nward.fit(X)\nlabel = np.reshape(ward.labels_, rescaled_coins.shape)\nprint(\"Elapsed time: \", time.time() - st)\nprint(\"Number of pixels: \", label.size)\nprint(\"Number of clusters: \", np.unique(label).size)\n\n# #############################################################################\n# Plot the results on an image\nplt.figure(figsize=(5, 5))\nplt.imshow(rescaled_coins, cmap=plt.cm.gray)\nfor l in range(n_clusters):\n    plt.contour(label == l,\n                colors=[plt.cm.nipy_spectral(l / float(n_clusters)), ])\nplt.xticks(())\nplt.yticks(())\nplt.show()"
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